Dominant group of factors that influence the brand market value, according to Aaker are: customer loyalty to the brand, perceived brand quality, brand familiarity and brand associations in comparison to competitors. Functional dependence between these factors and market brand value is not expressed in exact way, although these factors are quantitatively expressed with suitable index [17]. Modern technique of fuzzy logic and fuzzy sets implementation for problem solving in areas of finance and management is based on FLC (fuzzy logic control) process. Implementation of FLC process In this paper is represented in order to determine brand market value that is mathematical model is constructed using fuzzy numbers and fuzzy logic, which is used to quantitatively determine brand market value. Brand market value TV=f (L, P, K, A) is expressed depending on customer loyalty (L) towards the brand, perceived brand quality (K), brand familiarity (P) and brand association (A) received from the customers, where the measurement rates are evaluated using by fuzzy numbers.

U ovom radu analizirano je 15 europskih provenijencija običnog bora u međunarodnom pokusu kod Kupresa. Pokus je osnovan tijekom proljeća 2012. godine sa biljkama starosti dvije godine (2+0). Ukupno je posađeno 2700 sadnica običnog bora u pokusnom dizajnu slučajnog blok sustava koji je prilagođen terenu, 15 provenijencija u pet ponavljanja (5 x 36 sadnica). Pokus čine provenijencije iz deset europskih zemalja: Austrija (Traisen, Rein, Sistrans), Bosna i Heregovina (Bugojno), Ukrajina (Ivano Frankivsk), Slovačka (Hanusovce), Rumunjska (Sacueini), Norveška (Narvik, Arnes), Njemačka (Teisendorf, Trippstadt), Poljska (Raciane – Nida) i Italija (Ca del Lupo, Fenestrelle, Piani – Valda). Rezultati morfoloških i fizioloških istraživanja odnose se na razlike preživljivanja, visinama, promjer na vratu korijena i proljetnoj fenologiji otvaranja pupova.

Big data technologies have a strong impact on different industries, starting from the last decade, which continues nowadays, with the tendency to become omnipresent. The financial sector, as most of the other sectors, concentrated their operating activities mostly on structured data investigation. However, with the support of big data technologies, information stored in diverse sources of semi-structured and unstructured data could be harvested. Recent research and practice indicate that such information can be interesting for the decision-making process. Questions about how and to what extent research on data mining in the financial sector has developed and which tools are used for these purposes remains largely unexplored. This study aims to answer three research questions: (i) What is the intellectual core of the field? (ii) Which techniques are used in the financial sector for textual mining, especially in the era of the Internet, big data, and social media? (iii) Which data sources are the most often used for text mining in the financial sector, and for which purposes? In order to answer these questions, a qualitative analysis of literature is carried out using a systematic literature review, citation and co-citation analysis.

Introduction: Dietary supplements are an important source of vitamins and minerals that may help prevent several disease-causing biological pathways involved in one-carbon metabolism, including the suppression of cell proliferation, oxidative stress, nitric oxide synthesis, and angiogenesis. The present study aimed to assess the association between the intake of folate, Vitamin B6, Vitamin B12, and minerals and the consumption of alcohol among university students. Methods: This study was participated by students aged 19-22 years from the University of Sarajevo between 2017 and 2018. Using a questionnaire, we interviewed in a week them to collect information regarding age, socio-economic status, alcohol consumption, and dietary supplement intake. Then, we investigated the association between the baseline intake of folate, B vitamins, and minerals and that of alcohol consumption. Results: Most students consumed Vitamin B supplements (32%) and folic acid (10%). Dietary multivitamins and minerals were less prevalent in more than a year, accounting for 186 (23.9%) and 174 (24.3%) students, respectively, than those in less than a year. In a year, <20% of students consumed multivitamins 129 (16.6%) and minerals 116 (15.3%). Meanwhile, 256 (27.1%) students consumed alcohol. The Chi-square test of independence showed that drinking habits and the intake of such dietary supplements had no association (p > 0.05). Conclusion: An extremely low percentage of the participating students in Canton Sarajevo used dietary supplements of Vitamin B, folate, multivitamins, and minerals. Moreover, alcohol consumption and dietary supplement intake were not associated. Further research is needed to establish the best cost-effective public health system to achieve a sufficient intake of dietary supplements.

The main objective of this paper is to define the repositioning strategy of the Port of Adria, which is the leading container maritime port of Montenegro. The strategy is an integral reflection of the analysis of internal (competitive advantage and financial strength) and external (the potential of container maritime port industry and environmental stability) repositioning criteria. The case study in this paper is mainly accomplished through the definition of specific propositions that clarify the connections between these criteria and the repositioning strategy. Knowledge and attitudes of stakeholders are used with the purpose of modeling a marketing strategy, which is based on an inductive study. The paper proposes a model based on a specific maritime port case which can be applied to any other case of maritime port repositioning as well.

Vitamin D is one of the keys to bone health, and the serum levels of this vitamin are a major concern for postmenopausal women. The aims of this study were to develop a population pharmacokinetic (PPK) model for the clearance of 25-hydroxy vitamin D in non-elderly postmenopausal women and to identify the factors which have a significant influence on its clearance. The study population consisted of postmenopausal women who had been referred for evaluation of bone mineral density (BMD) by DEXA (dual-energy x-ray absorptiometry) scanner. The population pharmacokinetics modeling was conducted using the ADVAN 1 subroutine from a non-linear mixed effects (NONMEM) program, and thirty-two covariates were assessed. A total of 75 serum concentrations were obtained from the same number of postmenopausal women and used for PPK analysis. The mean value of the participantsí age was 57.92 ± 3.93 years and their body weight was 69.76 ± 11.49 kg. A wide range of 25-hydroxy vitamin D concentrations was observed (from 3.41 to 61.92 ng/mL) with a mean value of 26.19 ± 10.95 ng/mL. A total of 32 covariates were examined and preliminary results suggested the influence of six covariates on 25-hydroxy vitamin D clearance. In the final PPK model, however, only one covariate was shown to have a significant impact on the clearance value ñ the mean daily dietary intake dose of vitamin D (DD). These findings offer a preliminary basis on which to determine the level of vitamin D supplementation required by individual postmenopausal women. It could prove particularly important in achieving optimal serum levels of vitamin D in this vulnerable population.

The explanatory power of size, value, profitability, and investment has been extensively studied for equity markets. Yet, the relevance of these factors in global credit markets is less explored, although equities and bonds should be related according to structural credit risk models. In this article, the authors investigate the impact of the four Fama–French factors in the US and European credit space. Although all factors exhibit economically and statistically significant excess returns in the US high-yield market, the authors find mixed evidence for US and European investment-grade markets. Nevertheless, they show that investable multifactor portfolios outperform the corresponding corporate bond benchmarks on a risk-adjusted basis. Finally, their results highlight the impact of company-level characteristics on the joint return dynamics of equities and corporate bonds.

Abstract Background/purpose: To determine the efficacy and toxicity profile of a stereotactic body radiotherapy (SBRT) boost as a first line treatment in patients with oropharyngeal squamous cell carcinoma (OPSCC). Materials and methods: We performed a retrospective cohort study in 195 consecutive OPSCC patients with T1-small T3 disease, treated at Erasmus MC between 2009 and 2016 with a SBRT (3 × 5.5 Gy) boost after 46 Gy IMRT. Primary endpoints were disease-specific survival (DSS) and Grade ≥3 toxicity (Common Terminology Criteria). The Kaplan-Meier method and Cox regression model were applied to determine rates and risk factors. Results: The median follow-up was 4.3 years. Treatment compliance was high (100%). Rates of 5-year DSS and late grade ≥3 toxicity were 85% and 28%, respectively. Five-year overall survival was 67%. The most frequently observed toxicities were mucosal ulceration or soft tissue necrosis (n = 30, 5 year 18%), dysphagia or weight loss (n = 18, 5 year 12%) and osteoradionecrosis (n = 11, 5 year 9%). Current smoker status (hazard ratio [HR] = 2.9, p = .001) and Charlson Comorbidity Index ≥2 (HR = 1.9, p = .03) were was associated with increased toxicity risk. Tooth extraction prior to RT was associated with increased osteoradionecrosis risk (HR = 6.4, p = .006). Conclusion: We reported on outcomes in the largest patient series to date treated with a hypofractionated boost for OPSCC. Efficacy was good with survival rates comparable to conventionally fractionated (chemo)radiotherapy. Grade ≥3 toxicity profiles showed high rates of soft tissue necrosis and osteoradionecrosis. Strategies to mitigate severe toxicity risks are under investigation to improve the tolerability of the SBRT boost.

In this paper we will observe the model of competitive types and it will be analyzed using the nullcline method. It will be shown that this model has four points of equilibria, which are stable or unstable depending on the parameters a and b. The local stability of these points was investigated and global dynamics was determined using nullcline methods, that is, the bases of attraction of these points were shown.

Heat dissipation in next-generation electronics based on two-dimensional (2D) materials is a critical issue in their development and implementation. A potential bottleneck for heat removal in 2D-based devices is the thermal pathway from the 2D layer into its supporting substrate. The choice of substrate, its composition and structure, can strongly impact the thermal boundary conductance (TBC). Here we investigate the temperature-dependent TBC of 42 interfaces formed between a group of six 2D materials and seven crystalline and amorphous substrates. We use first-principles density functional perturbation theory to calculate the full phonon dispersion of the 2D layers and substrates and then input them into our model for phonon transport across the 2D–3D interface. Our results show that the TBC depends on the overlap between the vibrational frequencies and can be varied by nearly two orders of magnitude, from as low as  ∼0.6 MW · m K−1 (h-BN on diamond) to  ∼40 MW · m K−1 (h-BN on SiO2), for the same 2D layer by changing the substrate material. We find that amorphous materials significantly boost the TBC relative to their crystalline counterparts, assuming the two interfaces have the same adhesion, owing to the low-frequency Boson peak feature in their vibrational density of states (vDOS). For crystalline substrates, we further correlate constituent material properties with the calculated TBCs and find that the TBC strongly depends on a combination of the speed of sound, Debye temperature, and density of the substrate as well as the bandwidth of the flexural branch in the 2D material. We conclude that softer substrates with sharp low-frequency features in their vDOS, such as amorphous materials, polymers, and nanoparticles, could have higher TBC, leading to a trade-off between TBC and the thermal conductivity of the substrate.

7-Methyl-1,5,7-triazabicyclo[4.4.0]dec-5-ene (mTBD) has useful catalytic properties and can form an ionic liquid when mixed with an acid. Despite its potential usefulness, no data on its thermodynamic and transport properties are currently available in the literature. Here we present the first reliable public data on the liquid vapor pressure (temperature from 318.23 K to 451.2 K and pressure from 11.1 Pa to 10 000 Pa), liquid compressed density (293.15 K to 473.15 K and 0.092 MPa to 15.788 MPa), liquid isobaric heat capacity (312.48 K to 391.50 K), melting properties, liquid thermal conductivity (299.0 K to 372.9 K), liquid refractive index (293.15 K to 343.15 K), liquid viscosity (290.79 K to 363.00 K), liquid–vapor enthalpy of vaporization (318.23 K to 451.2 K), liquid thermal expansion coefficient (293.15 K to 473.15 K), and liquid isothermal compressibility of mTBD (293.15 K to 473.15). The properties of mTBD were compared with those of other relevant compounds, including 1,5-diazabicyclo(4.3.0)non-5-ene (DBN), 1,8-diazabicyclo[5.4.0]undec-7-ene (DBU), and 1,1,3,3-tetramethylguanidine (TMG). We used the PC-SAFT equation of state to model the thermodynamic properties of mTBD, DBN, DBU, and TMG. The PC-SAFT parameters were optimized using experimental data.

A car price prediction has been a high-interest research area, as it requires noticeable effort and knowledge of the field expert. Considerable number of distinct attributes are examined for the reliable and accurate prediction. To build a model for predicting the price of used cars in Bosnia and Herzegovina, we applied three machine learning techniques (Artificial Neural Network, Support Vector Machine and Random Forest). However, the mentioned techniques were applied to work as an ensemble. The data used for the prediction was collected from the web portal autopijaca.ba using web scraper that was written in PHP programming language. Respective performances of different algorithms were then compared to find one that best suits the available data set. The final prediction model was integrated into Java application. Furthermore, the model was evaluated using test data and the accuracy of 87.38% was obtained.